Ramesh Rajagopalan

Performance analysis of routing protocols in Zigbee non-beacon enabled WSNs

In recent years, much attention has been given to wireless sensor networks (WSNs) research, due to the growth in Micro-Electro-Mechanical Systems technology. IEEE 802.15.4 is starting to emerge as the next generation wireless standard for low-rate wireless personal area networks. Zigbee is a standard that builds upon IEEE 802.15.4 and offers low power, low data rate, and short range networking for wireless battery powered devices. The Ad hoc On-demand Distance Vector (AODV) routing protocol enables the routing of data between a source and destination in mesh networks. The Dynamic MANET On-demand routing protocol (DYMO) is a successor to AODV that was designed for wireless ad-hoc networks. In this paper, we present a performance comparison of AODV and DYMO based on the Zigbee standard. The performance comparison is based on the following metrics: packet delivery ratio, end-to-end delay, and energy consumption. Our work was implemented for realistic node densities and simulation times in non-beacon enabled Zigbee networks. Our simulations show that the performance of the protocols depend on the network topology. The results obtained indicate that DYMO outperforms AODV with respect to packet delivery ratio (PDR) and energy consumption.

Multi-objective optimization algorithms for sensor network design

Many sensor network design problems are characterized by the need to optimize multiple objectives. However, existing techniques in sensor network design generally optimize only one objective while treating the others as constraints or convert the multi-objective optimization problem into a single objective optimization problem using weights associated with different objectives. The weighted sum approach is subjective and is incapable of obtaining multiple tradeoff solutions for non-convex optimization problems. A multi-objective optimization approach optimizes all objectives simultaneously and obtains multiple tradeoff solutions for non-convex problems without the need for a weight vector. This paper illustrates this approach by formulating and solving the sensor placement problem for energy efficient target detection as a multi-objective optimization problem.

A multi-objective optimization approach for data fusion in Mobile Agent based Distributed Sensor Networks

A recent approach for data fusion in wireless sensor networks involves the use of mobile agents that selectively visit the sensors and incrementally fuse the data, thereby eliminating the unnecessary transmission of irrelevant or non-critical data. The order of sensors visited along the route determines the quality of the fused data and the communication cost. The computation of mobile agent routes involves tradeoffs between energy consumption, path loss, and detection accuracy. For instance, as the number of sensors in the route increases, the quality of fused data improves but the energy consumption and path loss increase. This paper models the mobile agent routing problem as a multi-objective optimization problem, maximizing the total detected signal energy while minimizing the energy consumption and path loss. A recently developed multi-objective evolutionary algorithm called the evolutionary multi-objective crowding algorithm (EMOCA) is employed for obtaining the mobile agent routes. The performance of EMOCA is compared with a recently proposed combinatorial optimization approach. Simulation results show that EMOCA outperforms the combinatorial optimization approach for different network sizes clearly demonstrating the advantage of a multi-objective optimization approach.

Energy efficient routing algorithm for patient monitoring in body sensor networks

Wireless body sensor networks are widely used for monitoring individuals in assisted living facilities and has emerged as a promising technology in e-healthcare. Such networks consist of sensors on the body or clothing of an individual for measuring vital signals such as heart beat, body temperature, and electrocardiogram. This enables patients to experience greater physical mobility and independence eliminating the need to stay in the hospital. Efficient and reliable transmission of data from on body sensors to medical personnel via multi-hop routing is critical for continuous health monitoring. In this paper, we propose a new routing algorithm for energy efficient routing in body sensor networks for reliable health monitoring. We model the routing problem as a constrained multi-objective optimization problem maximizing the throughput while minimizing the energy consumption subject to a constraint on end to end latency. We have designed a new constrained multi-objective genetic algorithm (CMOGA) for obtaining energy efficient routes. Simulation results show that CMOGA demonstrates the advantages of multi-objective optimization and outperforms a widely used and well known multi-objective evolutionary algorithm.

A multi-objective optimization approach for efficient energy management in smart grids

Smart grid uses bi directional flow of information to create a distributed and efficient energy delivery network. Some of the important objectives of a smart grid include improving energy efficiency, maximizing utility, reducing cost, and controlling emission. Smart grids use demand response as an effective strategy to address this challenge. Demand response uses real time scheduling to enable customers to modify their demand according to energy consumption costs. In this paper, we consider the problem of efficient scheduling of energy consumption of users in a smart grid. Efficient energy management involves tradeoffs between the cost associated with energy consumption and a utility function. The utility function can represent the living comfort of users or gross income of the utility company. The utility function is non decreasing with respect to total utilized power. Hence, it is important to understand the tradeoffs between energy consumption and utility. The main contribution of this work is the development of a multi-objective optimization framework for efficient energy scheduling in smart grids. A recently developed multi-objective evolutionary algorithm called the evolutionary multi-objective crowding algorithm (EMOCA) is adapted for simultaneously optimizing the energy cost and utility function subject to a constraint on the power generation capacity. Simulation results show that EMOCA demonstrates the advantages of multi-objective optimization and outperforms a widely used and well known multi-objective evolutionary algorithm.

Performance comparison of routing protocols in mobile ad hoc networks in the presence of faulty nodes

Efficient routing algorithms can provide significant benefits in a mobile ad hoc network in terms of higher throughput, lower end to end delay, and improved network performance. Many routing protocols have been proposed to efficiently discover paths from a source node to a destination node in a mobile ad hoc network. In this paper, we compare the performance of two well-known routing protocols: the ad hoc on demand distance vector routing protocol (AODV) and the optimized link state routing protocol (OLSR). Existing work has studied the routing protocols under idealistic settings where all nodes function properly. Our work addresses realistic settings where some nodes may be faulty resulting in the degradation of the performance of routing protocols. Extensive simulations were performed to analyze the impact of the number of faulty nodes on the performance of the routing protocols. Our results show that AODV outperforms OLSR in terms of packet delivery ratio while OLSR achieves a much lower end to end delay.

Path loss analysis in millimeter wave cellular systems for urban mobile communications

The proliferation in the number of mobile devices and developments in cellular technology has led to an ever increasing demand for mobile data. The global bandwidth shortage facing wireless carriers today has motivated research for fifth generation (5G) cellular systems. In recent years, millimeter wave (mmW) frequencies between 30 and 300 GHz are being considered as a promising technology for 5G systems. Such systems can offer superior user experience by providing data rates that exceed one Gigabit per second and latencies lower than a millisecond. However, there is little research about cellular mmW propagation in densely populated urban environments. Understanding the radio channel is a primary requirement for optimal design of mmW systems. Radio propagation in mmW systems faces significant challenges due to rapidly varying channel conditions and intermittent connectivity. In this paper, we study the propagation of mmW spectrum in an urban environment. We use a statistical model to simulate an urban environment with diverse building distributions. We perform extensive simulations to analyze the path loss behavior for both line of sight (LOS) and non line of sight (NLOS) conditions for 28 GHZ and 73 GHZ mmW frequencies. We observe that the path loss approximates a logarithmic fit for both LOS and NLOS environments. Our simulations show that the omnidirectional free space path loss is approximately 30 dB higher for mmW systems compared to current 3G PP cellular systems. To address this challenge, we propose using highly directional horn antennas with beam forming for reducing the path loss.

A hybrid algorithm for robust acoustic source localization in noisy and reverberant environments

Acoustic source localization using microphone arrays is widely used in videoconferencing and surveillance systems. However, it still remains a challenging task to develop efficient algorithms for accurate estimation of source location using distributed data processing. In this work, we propose a new algorithm for efficient localization of a speaker in noisy and reverberant environments such as videoconferencing. We propose a hybrid algorithm that combines generalized cross correlation based phase transform method (GCC-PHAT) and Tabu search to obtain a robust and accurate estimate of the speaker location. The Tabu Search algorithm iteratively improves the time difference of arrival (TDOA) estimate of GCC-PHAT by examining the neighboring solutions until a convergence in the TDOA value is obtained. Experiments were performed based on real world data recorded from a meeting room in the presence of noise such as computer and fans. Our results demonstrate that the proposed hybrid algorithm outperforms GCC-PHAT especially when the noise level is high. This shows the robustness of the proposed algorithm in noisy and realistic videoconferencing systems.